Advances In Neural Network For Credit Card Fraud Detection

Author(s)	Aditya Singh, Sakshi Srivastava
Country	India
Abstract	Credit card fraud is the most serious form of risk to financial security and it has a far-reaching effect on consumers and users in general; this makes it necessary that modern methods for machine learning like neural networks should be employed in order to improve their capacity and accuracy for detecting frauds. Various popular neural network architectures for fraud detection, including Feedforward Neural Networks, Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), Long Short-Term Memory Networks (LSTM), Autoencoders, and Generative Adversarial Networks (GAN) are investigated in this study.
Keywords	Feedforward Neural Networks (FNN), Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), Long Short-Term Memory Networks (LSTM), Autoencoders, Generative Adversarial Networks (GAN).
Field	Engineering
Published In	Volume 16, Issue 2, April-June 2025
Published On	2025-04-03
Cite This	Advances In Neural Network For Credit Card Fraud Detection - Aditya Singh, Sakshi Srivastava - IJSAT Volume 16, Issue 2, April-June 2025. DOI 10.71097/IJSAT.v16.i2.3236
DOI	https://doi.org/10.71097/IJSAT.v16.i2.3236
Short DOI	https://doi.org/g9drds

About IJSAT Fees & Payment Current Issue Publication Archive	Submit Research Paper Track Submission Status Publication Guidelines Publication Ethics Peer Review & Plagiarism	Join as a Reviewer Editors & Reviewers Reviewer Referral Program Get Reviewer Membership Certi.	Website/Journal Policies Usage Policy Content Policies Privacy Policy

Contact Us	Message on WhatsApp	+91-9687-182-185	editor@ijsat.org

International Journal on Science and Technology